In this paper, we investigate if an alternate DNN architecture, namely, graph neural networks (GNNs), can continue
to perform accurate and consistent SE after a topology change and without any subsequent re-training.
GNN is an ML model that
works on non-Euclidean
data defined as a graph [1]}.
The inputs of a GNN are the node feature and adjacency matrices which contain the node and connectivity information of the graph, respectively.
By employing graph-structured data, one can incorporate physical information of the system into the learning process of the GNN [2]}.
Hence, the GNN's learning process includes knowledge of the data (features) and the topology of the
system. This makes GNNs distinct from other ML models and more suited for handling topology changes.
